{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#导入魔法操作\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#画一个折现图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    -1.009454\n",
       "10   -1.269681\n",
       "20   -2.403181\n",
       "30    1.643900\n",
       "40    0.121191\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series(np.random.randn(10),np.arange(0,100,10))\n",
    "s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x84e72e8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q8OGRNeeKCQvm0x9fwXfnykJkwrekjQhnesaIIW+Xd0vIK6WWKKWKlVJHlFKP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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xacbf240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.017956</td>\n",
       "      <td>-0.489097</td>\n",
       "      <td>0.254607</td>\n",
       "      <td>-0.290217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.936432</td>\n",
       "      <td>0.550442</td>\n",
       "      <td>0.698090</td>\n",
       "      <td>1.873315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.273268</td>\n",
       "      <td>0.441055</td>\n",
       "      <td>2.515355</td>\n",
       "      <td>1.415183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2.154933</td>\n",
       "      <td>1.094456</td>\n",
       "      <td>0.972499</td>\n",
       "      <td>3.071067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1.547586</td>\n",
       "      <td>0.556744</td>\n",
       "      <td>0.912962</td>\n",
       "      <td>4.747138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           A         B         C         D\n",
       "0   1.017956 -0.489097  0.254607 -0.290217\n",
       "10  0.936432  0.550442  0.698090  1.873315\n",
       "20  3.273268  0.441055  2.515355  1.415183\n",
       "30  2.154933  1.094456  0.972499  3.071067\n",
       "40  1.547586  0.556744  0.912962  4.747138"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(10, 4).cumsum(0), \n",
    "               index = np.arange(0, 100, 10), \n",
    "               columns = ['A', 'B', 'C', 'D'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xad51780>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gionBb/ky3Dp1ynHbVtXL89eotnzQqSbbz8XQYdYefth/DbNFPPL7SdLy08vZ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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xad1f940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#画一个柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#画一个子图【竖着，横着】"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#子图需要画成什么样子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc730048>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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enbWzWkbSSjSu6qlRnMR0P/BoVX1k9nNWy0jSeFgtI0kdZLWMJHWQl9mTpA7yMnuS1EFt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\nRSFhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc673fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(2,1)\n",
    "data = pd.Series(np.random.rand(16),index=list('abcdefghijklmnop'))\n",
    "data.plot(ax = axes[0],kind='bar') #竖着画\n",
    "data.plot(ax = axes[1],kind='barh')#横着画"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df 如何画柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Genus</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0.864588</td>\n",
       "      <td>0.096330</td>\n",
       "      <td>0.595362</td>\n",
       "      <td>0.366106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.567723</td>\n",
       "      <td>0.414879</td>\n",
       "      <td>0.685371</td>\n",
       "      <td>0.578564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>0.030014</td>\n",
       "      <td>0.310003</td>\n",
       "      <td>0.055686</td>\n",
       "      <td>0.395542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>0.764631</td>\n",
       "      <td>0.962524</td>\n",
       "      <td>0.553370</td>\n",
       "      <td>0.770812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>0.348768</td>\n",
       "      <td>0.245817</td>\n",
       "      <td>0.962040</td>\n",
       "      <td>0.682288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Genus         A         B         C         D\n",
       "one    0.864588  0.096330  0.595362  0.366106\n",
       "two    0.567723  0.414879  0.685371  0.578564\n",
       "three  0.030014  0.310003  0.055686  0.395542\n",
       "four   0.764631  0.962524  0.553370  0.770812\n",
       "five   0.348768  0.245817  0.962040  0.682288"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(6, 4), \n",
    "               index = ['one', 'two', 'three', 'four', 'five', 'six'], \n",
    "               columns = pd.Index(['A', 'B', 'C', 'D'], name = 'Genus'))\n",
    "df.head()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc7030b8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JcribmSXI4W5mliCHu5lZgjzP3QaORSMPcmxP9eowS4B77mZmCXK4m5klyMMyZmb0srxC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\nf7jn/w1KiU4AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc5d2940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#导入数据进行画图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('./data/tips.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 244 entries, 0 to 243\n",
      "Data columns (total 7 columns):\n",
      "total_bill    244 non-null float64\n",
      "tip           244 non-null float64\n",
      "sex           244 non-null object\n",
      "smoker        244 non-null object\n",
      "day           244 non-null object\n",
      "time          244 non-null object\n",
      "size          244 non-null int64\n",
      "dtypes: float64(2), int64(1), object(4)\n",
      "memory usage: 13.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    244.000000\n",
       "mean      19.785943\n",
       "std        8.902412\n",
       "min        3.070000\n",
       "25%       13.347500\n",
       "50%       17.795000\n",
       "75%       24.127500\n",
       "max       50.810000\n",
       "Name: total_bill, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_bill'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#用其中一个列画图[直方图]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc857668>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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paz2ebwO6gI81vBpJ0oBR711MJze7EEnSwFLvENMXdvd6Zn69MeVIkgaKvtzFdAywpFj+\nG+Be4MlmFCVJql5fvjBoamZuAYiIK4HbMvOzzSpMklSteqfaGAu83mP5dWBcw6uRJA0Y9fYgvgc8\nGBF3UPtE9TnAd5tWlSSpcvXexXRVRPxf4Pii6VOZ+ZvmlSVJqlq9Q0wA+wEvZOY3gHURMb5JNUmS\nBoB6v3L0y8BlwOVF01DgfzerKElS9ertQZwDnA28BJCZ63GqDUlqafUGxOuZmRRTfkfE/s0rSZI0\nENQbEIsi4gbgoIj4e+Dn+OVBktTS6r2L6Zriu6hfACYC/yMzlzW1MklSpXoNiIgYAtydmR8C6g6F\niLgZOAvYmJmTiraDgVupfciuC/hYZv45IgL4BnAG8DJwQWY+3LdTkSQ1Uq9DTJm5HXg5Iv6yj/u+\nBZixU9s8YHlmTgCWF8sApwMTip/ZwPV9PJYkqcHq/ST1q8DqiFhGcScTQGZevKsNMvPeiBi3U/NM\n4KTi+QLgHmq3z84EvltcCP9VRBwUEaMyc0Od9UmSGqzegPhJ8bOnRnb/0s/MDRFxaNF+GDvODLuu\naDMgJKkiuw2IiBibmU9k5oIm1xElbbmLmmZTG4Zi7NixzaxJkga13q5B3Nn9JCJub8Dxno6IUcX+\nRgEbi/Z1wOE91hsDrC/bQWbemJkdmdkxYsSIBpQkSSrTW0D0/Mv+3Q043hJgVvF8FvDjHu1/FzXv\nB573+oMkVau3axC5i+e9iogfULsgfUhErAO+DMyn9qG7zwBPAOcWqy+ldovrWmq3uX6qL8eSJDVe\nbwExJSJeoNaTGFY8p1jOzHzHrjbMzPN28dIHS9ZNYE4d9UqS+sluAyIzh/RXIZKkgaUv3wchSRpE\nDAhJUikDQpJUyoCQJJUyICRJpQwISVIpA0KSVMqAkCSVMiAkSaUMCElSKQNCklTKgJAklTIgJEml\nDAhJUikDQpJUyoCQJJUyICRJpQwISVIpA0KSVMqAkCSV2rfqAjQ4jJv3k0qO2zX/zEqOK7UCexCS\npFIGhCSplAEhSSplQEiSShkQkqRSBoQkqZS3uQ4iVd1qKmnvZA9CklTKgJAklTIgJEmlDAhJUikD\nQpJUqpK7mCKiC9gCbAe2ZWZHRBwM3AqMA7qAj2Xmn6uoT5JUbQ/i5Mxsz8yOYnkesDwzJwDLi2VJ\nUkUG0hDTTGBB8XwB8OEKa5GkQa+qgEjgZxGxMiJmF20jM3MDQPF4aEW1SZKo7pPU0zJzfUQcCiyL\niN/Vu2ERKLMBxo4d26z6JGnQq6QHkZnri8eNwB3AscDTETEKoHjcuIttb8zMjszsGDFiRH+VLEmD\nTr8HRETsHxEHdj8HTgM6gSXArGK1WcCP+7s2SdJ/qGKIaSRwR0R0H39hZv40Ih4CFkXEZ4AngHMr\nqE2SVOj3gMjMfwemlLRvBj7Y3/VIksoNpNtcJUkDiAEhSSplQEiSShkQkqRSBoQkqZQBIUkqZUBI\nkkoZEJKkUlVN1if1i3HzflLZsbvmn1nZsaVGsAchSSplQEiSShkQkqRSBoQkqZQXqStQ5YVTSaqX\nPQhJUikDQpJUyoCQJJUyICRJpQwISVIpA0KSVMrbXKUmqep2ZueAUqPYg5AklTIgJEmlDAhJUikD\nQpJUyoCQJJXyLiapxXj3lBrFHoQkqZQBIUkqZUBIkkoZEJKkUl6krkBX2/mVHHfcqwsrOa6kvZM9\nCElSKXsQkhqiyu9a9xbb5hhwARERM4BvAEOAmzJzfsUlNdzk8WOrOfCaag4rNVuV4VSV/gjFATXE\nFBFDgP8FnA4cCZwXEUdWW5UkDU4DKiCAY4G1mfnvmfk68ENgZsU1SdKgNNAC4jDgyR7L64o2SVI/\nG2jXIKKkLXdYIWI2MLtYfDEift/LPg8BnmlAbXuTXZzzWf1eSD/zv/Xg4DkD8U97tL+/qmelgRYQ\n64DDeyyPAdb3XCEzbwRurHeHEbEiMzsaU97eYTCeMwzO8/acB4eqznmgDTE9BEyIiPER8Z+AjwNL\nKq5JkgalAdWDyMxtEXERcDe121xvzsxHKy5LkgalARUQAJm5FFjawF3WPRzVQgbjOcPgPG/PeXCo\n5JwjM3tfS5I06Ay0axCSpAGipQMiImZExO8jYm1EzKu6nmaIiJsjYmNEdPZoOzgilkXEY8XjO6us\nsdEi4vCI+EVErImIRyPi80V7y553RLRFxIMR8Uhxzl8p2sdHxK+Lc761uLmjpUTEkIj4TUTcVSy3\n9DlHRFdErI6IVRGxomir5L3dsgExiKbtuAWYsVPbPGB5Zk4AlhfLrWQb8MXMPAJ4PzCn+G/byuf9\nGnBKZk4B2oEZEfF+4J+Aa4tz/jPwmQprbJbPs+NMYoPhnE/OzPYet7ZW8t5u2YBgkEzbkZn3As/u\n1DwTWFA8XwB8uF+LarLM3JCZDxfPt1D75XEYLXzeWfNisTi0+EngFGBx0d5S5wwQEWOAM4GbiuWg\nxc95Fyp5b7dyQAzmaTtGZuYGqP0yBQ6tuJ6miYhxwHuBX9Pi510MtawCNgLLgD8Cz2XmtmKVVnyP\n/zPw34E3iuXhtP45J/CziFhZzBwBFb23B9xtrg3U67Qd2rtFxAHA7cAlmflC7Y/L1pWZ24H2iDgI\nuAM4omy1/q2qeSLiLGBjZq6MiJO6m0tWbZlzLkzLzPURcSiwLCJ+V1UhrdyD6HXajhb2dESMAige\nN1ZcT8NFxFBq4fD9zPxR0dzy5w2Qmc8B91C7/nJQRHT/oddq7/FpwNkR0UVtiPgUaj2KVj5nMnN9\n8biR2h8Cx1LRe7uVA2IwT9uxBJhVPJ8F/LjCWhquGIf+DrAmM7/e46WWPe+IGFH0HIiIYcCHqF17\n+QXwX4vVWuqcM/PyzByTmeOo/f/7/zLzb2nhc46I/SPiwO7nwGlAJxW9t1v6g3IRcQa1vzi6p+24\nquKSGi4ifgCcRG22x6eBLwN3AouAscATwLmZufOF7L1WRBwH3Aes5j/Gpq+gdh2iJc87Io6mdnFy\nCLU/7BZl5v+MiHdT++v6YOA3wCcy87XqKm2OYohpbmae1crnXJzbHcXivsDCzLwqIoZTwXu7pQNC\nkvT2tfIQkyRpDxgQkqRSBoQkqZQBIUkqZUBIkkoZEJKkUgaEJKmUASFJKvX/ASNp4Ce46BBfAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc5d2278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xce975c0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0ps9pSR5Ickv3evuk65QkPd4Ql6R2AW+pqpuTHA7clGRLVf3Non6fq6pzBqhPkjTGxM8w\nquq+qrq5W/4ecCdwzKTrkCQtz6D3MJKsAV4EfHHM5p9McmuSTyb5NxMtTJL0BIM9JZXkR4CPAW+u\nqgcXbb4ZeE5VPZRkHXANcPwS+1kPrAeYnZ3tsWJJWt0GOcNIcgijsPhwVX188faqerCqHuqWNwOH\nJDly3L6qamNVzVXV3MzMTK91S9JqNsRTUgEuA+6sqvcs0edZXT+SrGVU57cmV6UkabEhLkmdCvwy\ncFuSW7q2twGzAFV1KfBq4LeS7AIeAc6rqhqgVklSZ+KBUVWfB7KHPpcAl0ymIklSCz/pLUlq4lxS\n0gFgf5p7bLnzJy13bNM6D9OBwDMMSVITA0OS1MTAkCQ1MTAkSU0MDElSEwNDktTEwJAkNTEwJElN\nDAxJUhMDQ5LUxKlBOis1XYHTEmh/t9ypOFbq/4W+pzfZmzqnbVqSof/e8QxDktTEwJAkNTEwJElN\nDAxJUhMDQ5LUxMCQJDUxMCRJTQYJjCRnJflKknuSbBiz/V8kubrb/sUkayZfpSRpoYkHRpKDgD8E\nzgZOBM5PcuKibq8DvlNV/wq4GPjvk61SkrTYEGcYa4F7qureqnoU+GPg3EV9zgWu7Jb/FHhpkkyw\nRknSIkMExjHA1xasb+/axvapql3AA8AzJlKdJGmsVNVkD5i8BvjZqnp9t/7LwNqq+vcL+tzR9dne\nrX+16/OtMftbD6zvVk8AvtLzEKbFkcA3hy5iQKt5/Kt57OD4V3r8z6mqmZaOQ0w+uB04bsH6scCO\nJfpsT3Iw8KPAt8ftrKo2Aht7qHOqJdlaVXND1zGU1Tz+1Tx2cPxDjn+IS1JfAo5P8twkTwbOA65d\n1Oda4IJu+dXAn9ekT4UkSY8z8TOMqtqV5I3Ap4GDgMur6o4k7wS2VtW1wGXAh5Lcw+jM4rxJ1ylJ\nerxBvg+jqjYDmxe1vX3B8j8Br5l0XfuZVXcZbpHVPP7VPHZw/IONf+I3vSVJ+yenBpEkNTEw9gNJ\nLk+yM8ntC9qenmRLkru7n08bssa+JDkuyWeT3JnkjiRv6tpXy/ifkuTGJLd24/8vXftzu2lz7u6m\n0Xny0LX2JclBSb6c5P9266tp7NuS3JbkliRbu7bBfvcNjP3DFcBZi9o2AJ+pquOBz3TrB6JdwFuq\n6vnAS4ALu6lkVsv4vw+cUVUnAScDZyV5CaPpci7uxv8dRtPpHKjeBNy5YH01jR3g9Ko6ecGjtIP9\n7hsY+4GquoEnfg5l4fQpVwL/bqJFTUhV3VdVN3fL32P0F8cxrJ7xV1U91K0e0r0KOIPRtDlwAI8/\nybHAK4APdOthlYx9Nwb73Tcw9l9HVdV9MPpLFXjmwPX0rpu1+EXAF1lF4+8uydwC7AS2AF8FvttN\nmwPjp9c5ULwX+E/AD7r1Z7B6xg6jfxxcn+SmblYLGPB3f5DHaqXlSvIjwMeAN1fVg6tpLsqqegw4\nOckRwCbg+eO6Tbaq/iU5B9hZVTclOW2+eUzXA27sC5xaVTuSPBPYkuSuIYvxDGP/dX+SowG6nzsH\nrqc3SQ5hFBYfrqqPd82rZvzzquq7wF8wupdzRDdtDoyfXudAcCrwyiTbGM1qfQajM47VMHYAqmpH\n93Mno38srGXA330DY/+1cPqUC4BPDFhLb7pr1pcBd1bVexZsWi3jn+nOLEjyVOBljO7jfJbRtDlw\ngI6/qt5aVcdW1RpGsz38eVW9llUwdoAkhyU5fH4ZOBO4nQF/9/3g3n4gyVXAaYxmqbwfuAi4Bvgo\nMAv8A/Caqho7QeP+LMlPAZ8DbuOH17Hfxug+xmoY/wsZ3dg8iNE/8D5aVe9M8jxG/+p+OvBl4Jeq\n6vvDVdqv7pLU71TVOatl7N04N3WrBwMfqarfT/IMBvrdNzAkSU28JCVJamJgSJKaGBiSpCYGhiSp\niYEhSWpiYEiSmhgYkqQmBoYkqcn/B4iL0xmJURfCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcc70358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.total_bill.plot(kind='hist',bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcdc9518>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xcf70048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.total_bill.plot(kind='hist',bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc345400>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3D+P/b+B5U6yxtQuAXcvWexo7wKOr6rRll2JO7b1v4G+CqroC+N5em58MXDQsXwT81qYW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\nHmm1bPBAuVEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe20bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.total_bill.plot(kind='hist',bins=244)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>1707.4</td>\n",
       "      <td>286.898</td>\n",
       "      <td>470.045</td>\n",
       "      <td>1886.9</td>\n",
       "      <td>28.98</td>\n",
       "      <td>139.7</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>177.146</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>1733.7</td>\n",
       "      <td>310.859</td>\n",
       "      <td>481.301</td>\n",
       "      <td>1919.7</td>\n",
       "      <td>29.15</td>\n",
       "      <td>141.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.1</td>\n",
       "      <td>177.830</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>1751.8</td>\n",
       "      <td>289.226</td>\n",
       "      <td>491.260</td>\n",
       "      <td>1916.4</td>\n",
       "      <td>29.35</td>\n",
       "      <td>140.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.3</td>\n",
       "      <td>178.657</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>1753.7</td>\n",
       "      <td>299.356</td>\n",
       "      <td>484.052</td>\n",
       "      <td>1931.3</td>\n",
       "      <td>29.37</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179.386</td>\n",
       "      <td>0.27</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>1770.5</td>\n",
       "      <td>331.722</td>\n",
       "      <td>462.199</td>\n",
       "      <td>1955.5</td>\n",
       "      <td>29.54</td>\n",
       "      <td>139.6</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>180.007</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  quarter   realgdp  realcons  realinv  realgovt  realdpi    cpi  \\\n",
       "0  1959.0      1.0  2710.349    1707.4  286.898   470.045   1886.9  28.98   \n",
       "1  1959.0      2.0  2778.801    1733.7  310.859   481.301   1919.7  29.15   \n",
       "2  1959.0      3.0  2775.488    1751.8  289.226   491.260   1916.4  29.35   \n",
       "3  1959.0      4.0  2785.204    1753.7  299.356   484.052   1931.3  29.37   \n",
       "4  1960.0      1.0  2847.699    1770.5  331.722   462.199   1955.5  29.54   \n",
       "\n",
       "      m1  tbilrate  unemp      pop  infl  realint  \n",
       "0  139.7      2.82    5.8  177.146  0.00     0.00  \n",
       "1  141.7      3.08    5.1  177.830  2.34     0.74  \n",
       "2  140.5      3.82    5.3  178.657  2.74     1.09  \n",
       "3  140.0      4.33    5.6  179.386  0.27     4.06  \n",
       "4  139.6      3.50    5.2  180.007  2.31     1.19  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro = pd.read_csv('./data/macrodata.csv')\n",
    "macro.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=macro[['quarter','realgdp','realcons']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe4a8588>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mo3iUgcWjbuKY1vduY/W193PlnU+w+tr7C596n7el8i7gXDN7Jnu+DHjSzH4EuLsfX0js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\nIlI3KlRERKRuVKiIiEjdqFAREZG6+f8DNrAM13OBIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe415780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot.scatter('quarter','realgdp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pd"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
